Abstract
Purpose:
To develop a simple and high throughput method for the molecular imaging of proteins at the blood‐brain barrier using immunohistochemistry quantification.
Methods:
C57B6 male mice, age 4 months, were exposed to one of four different conditions for 12 hours/day × 2 weeks (a) Sham‐‐exposure to constant air‐‐e.g. 21% oxygen (b) cyclical intermittent hypoxia (CIH) from 21% to 12% (c) CIH from 21% to 10% and (d) CIH from 21% to 5%. Immunohistochemistry was then optimized for proteins GLUT1, p‐glycoprotein and CD31 on blood vessels at the bloodbrain barrier. The image data sets of four different conditions were subjected to our image analysis algorithm. Background noise is eliminated using thresholding techniques and blood vessel candidates are delineated using methods based on connected component analysis. Subsequently, relevant features are extracted from the candidate set and are passed through our classification algorithm for capillary identification. We then run a reinforcement algorithm on a subset of images to improve identity classification. This is accomplished by calculating the false positive/negative rate of the subset and feeding these results back to the classification algorithm. We introduce the idea that the gold standard should be computer + human vision.
Results:
For GLUT1 we analyzed over 5000 images and identified over 400k blood vessels with a false positive rate of 4.1%. For p‐glycoprotein we analyzed over 5000 images and identified over 400k blood vessels with a false positive rate of 5%.
Conclusion:
Our method provides quantitative measurements of proteins on blood vessels at the blood‐brain barrier in an objective, reproducible and comparable way using standard immunohistochemistry techniques and bright‐field microscopy.
